44 results about "State predictor" patented technology

The invention provides a chip production logistics network optimization method and system based on a graph neural network model, and relates to the technical field of chip production logistics optimizing.The method comprises the steps that logistics data of a plurality of process nodes of a chip production line are collected, node criticality scores are calculated, node types are determined, and the node types are determined; constructing a multi-level logistics network diagram comprising a material transmission edge and a priority-based resource competition edge; the method comprises the following steps: dividing logistics sub-networks, determining association strength among the sub-networks, constructing a sub-network state predictor and a cooperative scheduling module, and inputting a network topology structure and node features into a graph neural network for training to obtain an optimization model; and predicting a sub-network state change based on the optimization model to generate a resource allocation scheme and a node scheduling sequence, triggering an adjustment mechanism when a prediction state exceeds a threshold value, re-planning a material allocation and transmission path, and finally converting the scheme into an equipment control instruction and issuing the equipment control instruction to a process node.

The invention provides an industrial robot multi-station intelligent sorting and stacking method based on visual guidance, which relates to the technical field of robot control, and comprises the following steps: determining a target station by acquiring multi-station real-time state information, calculating an optimal grabbing time window by using a graph neural network state predictor, and obtaining a target grabbing time window; and the placement strategy is dynamically adjusted based on the visual image and the force sensing data in the article stacking process. According to the multi-station intelligent stacking system, the sorting efficiency in a multi-station scene can be improved, the grabbing precision of dynamic objects is enhanced, and stable and reliable intelligent stacking operation is achieved.

The invention provides a glyceryl triacetate production parameter control method and system based on reinforcement learning, and relates to the technical field of optimization control methods, and the method comprises the steps: obtaining the production parameters of glyceryl triacetate; based on the kinetic model, constructing a kinetic equation of the esterification reaction of glycerol and acetic acid; constructing a digital twinborn simulation environment of the glyceryl triacetate production process; defining a reinforcement learning framework by taking a digital twin simulation environment as a scene; according to the reinforcement learning framework, taking the Elman neural network as a state predictor, and predicting the state of the glyceryl triacetate; solving the reinforcement learning framework through an MO-MPO algorithm, and determining an optimal control strategy and an optimal production parameter sequence corresponding to the optimal control strategy; judging whether the process capability index is smaller than a process capability index threshold value or not; if yes, continuing to solve the reinforcement learning framework; otherwise, outputting an optimal production parameter sequence; and controlling the optimal production parameter sequence through a PID (Proportion Integration Differentiation) control algorithm.

A predictor 62 of a traffic safety support system includes a movement state information acquirer 620 configured to acquire movement state information of the prediction target, a surrounding state information acquirer 621 configured to acquire surrounding state information, a first movement state predictor 624 configured to predict a first predicted movement state on the basis of the movement state information, a second movement state predictor 625 configured to predict a second predicted movement state on the basis of the driver state information, a collision risk calculator 626 configured to calculate a first collision risk value for the first predicted movement state and a second collision risk value for the second predicted movement state, and a support controller 65 configured to execute support control for the prediction target in a case where at least one of the first and second collision risk values is greater than a predetermined threshold value.

The invention discloses a floating type draught fan power control method and device based on wave interval estimation and a medium, and relates to the technical field of draught fan control. The method comprises the steps that an observed value of a state variable at the current moment and a predicted value of a control input quantity at the previous moment are obtained; determining an estimated value of wave interference at the current moment; determining a predicted value of the wave interference at the next moment based on the estimated values of the wave interference at the current moment and the previous moment by using a wave interference predictor; using a state predictor to determine a predicted value of a state variable at the next moment based on the observed value of the state variable at the current moment and a predicted value of wave interference at the next moment, and using a time delay controller to determine a predicted value of a control input quantity at the current moment; and controlling the floating fan at the current moment by using the predicted value of the control input quantity at the current moment. According to the invention, accurate compensation and control of wave interference are realized.

The invention discloses an electric cylinder friction force parameter and state simultaneous estimation method and device and a medium. Aiming at the stick-slip phenomenon and the oscillation phenomenon caused by friction force in low-speed movement, the friction force model of the servo electric cylinder is constructed by combining a traditional static friction force model and a dynamic LuGre friction force model. And on the basis, according to a moment balance equation, a dynamic model of the servo electric cylinder is established. A motion state predictor of the servo electric cylinder is designed by means of a kinetic model and a friction force model of the servo electric cylinder. And updating a friction force state estimation error by using the motion state prediction error, designing a friction force state observer, and estimating the friction force state. In combination with the estimation of the friction force state, the self-adaptive law of the friction force model parameters is constructed, and the friction force model parameters are estimated online, so that the precise estimation of the friction force borne by the servo electric cylinder is realized, and the precise compensation effect of the servo electric cylinder system on the friction force is realized.

The invention provides a dynamic risk perception prediction stability control system and method for a cold-chain logistics transport vehicle, and belongs to the technical field of vehicle control. Comprising three core modules: a physical-data mixed residual state predictor, a quantitative stability evaluation module based on a maximum Lyapunov index, and a self-adaptive nonlinear model prediction controller fusing predetermined performance control. And finally, the control instruction is distributed to the four in-wheel motors through a multi-target torque distributor. According to the method, the prediction precision is remarkably improved, continuous risk quantification is realized, and meanwhile, the control performance and the system robustness are synchronously improved.

The invention discloses a communication delay-oriented multi-spacecraft cooperative rendezvous control method, which comprises the following steps of: firstly, respectively designing two delay state predictors for each follower spacecraft, and effectively solving the problem of transmission delay of output states between adjacent spacecrafts induced by communication bandwidth constraints; based on two time delay state predictors, designing a distributed composite state observer to perform real-time estimation on state information, control input, relative state of the observer and the leader and unknown sum disturbance of the leader; and a distributed cooperative rendezvous controller is designed by using an estimated output value of the distributed composite state observer, so that no-error tracking of the follower spacecraft on the state of the leader is realized while the total disturbance is inhibited, and cooperative rendezvous of the whole spacecraft cluster is realized. According to a cooperative control strategy proposed for a spacecraft cluster system, autonomous cooperative rendezvous of spacecraft clusters is realized by using less transmission information with time delay, and the method can be widely applied to other multi-agent formation cooperative systems.

The present invention provides an adaptive optimization thermal management control method, system and thermal management system for a liquid-cooled power source, realizing high-performance temperature control under varying working conditions and ensuring the efficient and stable operation of the power source. The time-delay estimation module and the state predictor module accurately estimate the system time delay and the real-time delay-free state inside the system, compensating for performance decays such as temperature overshoot and slow response; aiming at the measurement and estimation problems of the heat generation of the power source and the heat dissipation of the radiator under varying working conditions, the disturbance estimator measures the coolant temperature information at the outlets of the power source and the radiator to accurately estimate the uncertain heat exchange amount of the system, saving costs, and aiming at the problems of energy consumption optimization and internal temperature consistency of the thermal management system; the equilibrium point optimization module calculates the working point of the radiator outlet temperature corresponding to the optimal energy consumption of the system on the premise of meeting the temperature consistency requirement; aiming at the high-performance temperature tracking control requirement, the state tracking control module can achieve fast-response and high-precision temperature control.

The invention discloses a shield tunnel segment intelligent cooperation method and system based on dynamic damage prediction and self-repairing. The method comprises the steps that digital twin bodies synchronously mapped with a physical tunnel entity are constructed; acquiring strain and temperature data streams, performing data cleaning and feature extraction, and inputting the data streams to the digital twinborn body; real-time simulation is carried out to diagnose the damage state of the physical segment, and a first-level decision node is triggered: a critical state predictor is called, the remaining time T from damage to the critical state is dynamically calculated, a second-level decision node is triggered, and a repair task identifier is generated; the repair strategy optimizer decides an optimal repair scheme and generates a control instruction set, and the mobile repair robot is driven to execute automatic repair operation; and evaluating the repairing efficiency and carrying out self-learning updating. According to the invention, by fusing the real-time sensing data and the multi-factor damage model, accurate diagnosis and dynamic risk prediction of the structural damage are realized, the early warning capability is greatly improved, and dynamic decision and continuous optimization are realized.

The invention discloses an unmanned aerial vehicle reinforcement learning control method and device based on spatial-temporal feature separation, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: separating collected unmanned aerial vehicle sensor data into kinematic features and dynamic features; inputting the kinematic features and the dynamic features into a delay compensation state predictor, and outputting a delay compensation state; performing fluid mechanics constraint verification on the time delay compensation state, and if verification is not passed, performing gradient guide correction on the time delay compensation state; performing feature splicing on the final time delay compensation state and the target task state of the unmanned aerial vehicle, and inputting the spliced features into a strategy network to obtain a control instruction of the unmanned aerial vehicle; the strategy network is obtained through self-adaptive distillation framework training, and the teacher network and the student network are trained in an ideal environment and a delayed environment respectively; the teacher network and the student network are trained by adopting a reinforcement learning algorithm. According to the invention, the attitude recovery time and energy loss of the unmanned aerial vehicle in an interference environment can be reduced.

The present application belongs to the technical field of hot press tank temperature control in composite material forming process, and specifically discloses a kind of inflection point smooth transition method and system of hot press tank temperature control curve, comprising: superimposed test disturbance signal and collected response data before reaching temperature control inflection point;Local time constant and steady-state process gain are identified by constructing mathematical model;The parameter is converted into hard constraint boundary, and the dynamic time sequence transition trajectory sequence is generated by combining the slope before and after inflection point solution;Future desired temperature prediction value is generated using state predictor;Predictive control bias is calculated based on trajectory sequence and prediction value, and trajectory change rate is extracted;Accordingly, proportional integral adjustment component and feedforward compensation component are calculated, and are combined into execution control quantity to drive hot press tank.The present application eliminates temperature overshoot and response delay, and realizes inflection point smooth transition.

The application discloses a high-dynamic funnel control method based on a state predictor and belongs to the technical field of electromechanical control. The application mainly comprises the following three parts: firstly, a differential tracker is designed to arrange the dynamic process of a high-dynamic centrifuge system, to smoothly track a target curve, to prevent the high-dynamic centrifuge system from having excessive overshoot, to realize smooth transition of the dynamic process of the system, and to reduce the control difficulty of a subsequent controller; secondly, an adaptive funnel controller is designed, which is more simple in structure and non-singular, guarantees that the dynamic and steady-state responses of tracking errors are limited in a given funnel boundary, improves the dynamic response of the system, and reduces overshoot and regulation time; and finally, a state predictor is used to adjust the adaptive control process, so as to accelerate the convergence speed and smooth the convergence curve. The application can improve the dynamic performance of the centrifuge system under the conditions of high dynamic, high g value and high lift rate, and realize accurate control of the precision centrifuge system.

The invention provides a road noise reduction control method and system based on traffic state statistics, and relates to the field of road noise reduction control, and the method comprises the steps: collecting a real-time traffic state and a real-time associated traffic state of a target road, and predicting and obtaining a predicted traffic state of the target road in a future time period through a traffic state predictor; through a noise feature simulation channel, a simulated noise feature sequence is obtained through analysis according to the predicted traffic state; and setting a reverse sound wave control scheme of the active noise reduction equipment by taking elimination of the analog noise feature sequence as a target, and executing noise reduction control. The objective of the invention is to solve the technical problem that the timeliness and precision of road noise suppression are insufficient due to the fact that a traditional noise reduction control method is difficult to cope with the dynamic change of road traffic noise, and can significantly improve the timeliness and precision of noise reduction scheme setting through real-time traffic prediction and dynamic reverse sound wave control. The problems of time-varying characteristics and complexity of road traffic noise are effectively solved, and accurate road noise reduction control is realized.

The application discloses a kind of intelligent wireless link state prediction methods, first, establish MIMO-OFDM system model;Then propose a kind of Transform time sequence association encoder and the asynchronous advantage actor-critic algorithm (Asynchronous Advantage Actor-Critic, A3C) reinforcement learning framework of adapted online updating;Finally, the corresponding wireless link state predictor is designed.According to the equivalent SINR in system model and the modulation and coding scheme (Modulation and Coding Scheme, MCS) calculation mode simulation verification, via public real MIMO-OFDM operation data set verification, the application has good MCS prediction accuracy, applicable to large-scale MIMO system link prediction.

The present invention discloses a state machine circuit detection device and method. The state machine circuit detection device includes: a state predictor, a state jump capturer and a state comparator; the state predictor is used to: respond to the input state jump condition information, predict multiple predicted states of the state machine circuit to be detected; the state jump capturer is used to: monitor the real-time state of the state machine circuit, and capture the real-time state when the state of the state machine circuit jumps, and send first information to the state comparator; the state comparator is used to: after receiving the first information, compare whether a specific predicted state among the multiple predicted states is consistent with the real-time state. The state machine circuit detection device and method provided by the present invention can reduce the time cost of state machine circuit detection.

The application discloses a lithium battery health state and residual service life joint prediction method based on adversarial learning, and belongs to the lithium battery life prediction field. The application firstly performs simple preprocessing on original aging data, then utilizes a shared feature extractor and a specific feature extractor composed of a convolution network module and a residual network module to extract task-shared features and task-specific features of the health state and the residual service life based on adversarial learning, and improves feature discrimination; after the task-shared features and the task-specific features of the health state and the residual service life are fused, the fused features are input into a health state predictor and a residual service life predictor, model negative optimization caused by feature confusion is avoided, and finally, health state and residual service life prediction results are obtained. The application carries out the lithium battery health state and residual service life joint prediction based on adversarial learning, the joint prediction model has low requirements for input, the prediction error is low, and the aging state and the life state of the lithium battery in an actual use scenario can be effectively monitored, and the safety of equipment is improved.

The application discloses a floating wind turbine power control method and device based on wave interval estimation and a medium, relates to the technical field of wind turbine control, and comprises the following steps: acquiring an observation value of a state variable at a current moment and a predicted value of a control input at a previous moment, and determining an estimated value of wave disturbance at the current moment by using an interference interval observer; determining a predicted value of wave disturbance at a next moment based on the estimated values of wave disturbance at the current moment and the previous moment by using a wave disturbance predictor; determining a predicted value of the state variable at the next moment based on the observation value of the state variable at the current moment and the predicted value of wave disturbance at the next moment by using a state predictor, and determining a predicted value of the control input at the current moment by using a time delay controller; and controlling the floating wind turbine at the current moment by using the predicted value of the control input at the current moment. The application realizes accurate compensation and control of wave disturbance.

The invention relates to the technical field of intelligent compensation control, in particular to a linear motor driving system control method based on robust baseline control and adaptive compensation. The method comprises the following steps: establishing a linear motor state space model; a dynamic output feedback baseline controller is obtained by iteratively solving a Riccati equation set based on a disturbance suppression level index, a closed-loop matrix is extracted, and a baseline control signal is output; calculating a steady-state gain and filtering the reference input to generate a steady-state feed-forward control signal; estimating the total uncertainty on line through a state predictor, constructing a projection operator to decompose the total uncertainty into a matched component and an unmatched component, performing output equivalent mapping on the unmatched component, and performing band-limiting processing through a low-pass filter to synthesize a self-adaptive compensation signal; and after the three paths of signals are superposed, the linear motor is driven through amplitude saturation and change rate limitation. According to the method, the robustness and the online compensation capability are fused, and the tracking precision and the anti-interference robustness of the linear motor are remarkably improved.

The invention discloses a predetermined performance control method of a dual-motor servo system considering external disturbance. The method comprises the following steps: establishing a kinetic model of the dual-motor servo system considering the external disturbance; defining total disturbance and state variables of the dual-motor servo system; according to the total disturbance and the state variable of the dual-motor servo system, the kinetic model of the dual-motor servo system considering the external disturbance is transformed, and a state equation of the dual-motor servo system is obtained; constructing a radial basis function neural network to respectively estimate load end total disturbance and motor end total disturbance, and obtaining approximation values of the load end total disturbance and the motor end total disturbance; according to a specified performance function, a prediction tracking error constraint condition is established, and a transformation error is solved according to an introduced smooth strict increasing function of the transformation error; and designing a predetermined performance controller based on a state predictor in combination with the tracking controller and the synchronous controller. According to the method, the motors are tracked on the premise of ensuring the synchronization of the two motors, and the stability of the system is proved through the Lyapunov criterion.

A method for training an eye state predictor (ESP) implemented as a neural network, comprising feeding a first eye-related observation (ERO, ERO1, Pl, Pr) as input to the eye state predictor (ESP) to determine as output of the eye state predictor (ESP) a predicted 3D eye state (3DES, {EC, EG, EP}) of at least one eye of a subject within a time (tk), the first eye-related observation (ERO, EROi, Pi, Pr) being connected to the first eye-related observation (ERO, ERO1, Pl, Pr). Pr) relates to at least one eye of the subject within a time (tk). A predicted 3D eye state ({EC, EG, EP}) is fed as input to a differentiable predictor (DP) to determine a prediction (pi) of at least one eye of the subject within a time (tk) as output of the differentiable predictor (DP). A training loss (delta) is determined based on the prediction (pi) and at least one of the first eye-related observation (ERO, ERO1, P1, Pr) and the second eye-related observation. The second eye-related observation (ERO2) involves at least one eye of the subject within a time (tk). The training loss (delta) is used to train an eye state predictor (ESP).

The invention discloses a brushless direct current motor control method, particularly relates to a brushless direct current motor constant-speed motion control method for underground broken pipe detection, ensures that an underground broken pipe detection device realizes high-precision constant-speed motion control, and belongs to the technical field of underground broken pipe detection. According to the invention, a system state observer and a control state predictor are designed based on arc tangent dynamic saturation constraint and a time delay state predictor, so that the problems of measurement outlier signals induced by underground complex environments such as electromagnetic interference and the like of a brushless direct current motor rotating speed sensor and time delay caused by long-distance signal transmission can be effectively solved. In addition, the estimated value of the total disturbance is compensated to the state feedback controller, the adverse effect of the total disturbance on the system is restrained, and the reliability and the detection precision of the underground broken pipe detection device are improved. The control strategy provided by the invention has a good control effect on the underground casing damage detection device, and can be widely applied to control systems of other long-distance detection devices.

Systems and methods for determining the total chargeable / dischargeable energy of subsystems of a battery energy storage system (BESS) are disclosed. An iterative process is performed within a dynamic period of time divided into multiple iterations using a neural network model including an energy prediction sub-model and a state prediction sub-model. The energy prediction sub-model outputs chargeable / dischargeable energy for the subsystem of the current iteration. The state prediction sub-model outputs a voltage of the subsystem for the next iteration, a charge rate of the subsystem for the next iteration, a maximum temperature of the subsystem for the next iteration, and a charge rate difference for the next iteration. The total chargeable / dischargeable energy of the BESS subsystem is determined by summing the dischargeable energy determined for each iteration.

The invention discloses an integrated pole voltage and current sensor. The integrated pole voltage and current sensor comprises a sensor measuring unit; a dynamic state predictor; a nonlinear characteristic modeler; a parameter interaction optimizer; an adaptive learning controller; a calibration output unit; the dynamic state predictor and the nonlinear characteristic modeling device form a closed-loop deep fusion structure through the parameter interaction optimizer to realize bidirectional information flow interaction, the particle filtering algorithm provides dynamic state information to control neural network training, and the neural network algorithm provides a nonlinear characteristic model to enhance particle filtering performance. The integrated pole voltage and current sensor has the beneficial effects that the technical problems of fast measurement precision attenuation, dynamic response lag, poor environmental adaptability, high calibration cost and the like of a traditional sensor can be effectively solved, and the purposes of improving the static measurement precision, shortening the response time, reducing the number of particles and improving the measurement precision are achieved. And the calculation efficiency is obviously improved.

The invention relates to the technical field of intelligent compensation control, in particular to a linear time-invariant ball screw driving system control method based on self-adaptive control. The method comprises the following steps: establishing a state space model of a system; designing a baseline controller, obtaining a state feedback gain by solving a linear matrix inequality, and generating a baseline control signal; calculating a steady-state gain, and filtering the reference input signal to generate a steady-state feed-forward control signal; constructing a state predictor, estimating the total uncertainty on line, decomposing the total uncertainty into matching and mismatching signals, generating matching and mismatching compensation signals after gain mapping and low-pass filter band limiting processing, and synthesizing the matching and mismatching compensation signals into self-adaptive compensation signals; and superposing the three paths of signals to obtain a master control signal, and after amplitude saturation and change rate limitation, outputting a system to input a signal driving system. According to the control method provided by the invention, the tracking precision of the ball screw system and the resistance to uncertain factors and external disturbance are effectively improved.

State of art techniques relate to telepresence robots expressing internal states using emotional expressions and corresponding text messages are based on the perceived input from the co-located user or the participant, and not related to the teleoperator. Embodiments of the present disclosure provide a method and system for expressing telepresence robot internal states using combination of multiple modalities. The telepresence robot expresses its own internal states in the form of a plurality of emotional expressions and corresponding text messages to the teleoperator, using a robot internal state predictor model. Unlike the state of art techniques which express an emotion as its internal state, the disclosed method expresses the internal state as the emotional expression with respect to a task processing of the telepresence robot.

The present invention discloses a multi-heterogeneous agent anti-interference cooperative control algorithm for a polling protocol, and in particular, relates to a multi-heterogeneous agent anti-interference cooperative control method under a polling protocol, and belongs to the field of multi-agent system cooperative control; the present invention first reduces the information interaction between neighboring agents by designing a polling protocol, reduces the network burden, and designs a polling inter-state predictor to predict the state information of neighboring agents that do not interact with information, thereby overcoming the contradiction between limited information interaction and control accuracy; a distributed extended state observer is designed using the state information of agents between neighbors, and the state in the system and the total disturbance caused by unknown nonlinearity, external interference, etc. are estimated in real time, and the estimated value of the total disturbance is compensated to the error feedback controller to form a composite distributed controller, thereby improving the robustness of the entire system and ensuring high-precision control of the multi-agent system. The control strategy proposed by the present invention has a good control effect on the multi-agent control system, and can be widely used in multi-agent control systems under other communication protocols.

The embodiments of this specification provide a method and device for power system frequency control under a deterministic network delay attack. The method includes: sending a signal to the deterministic network through a super-twisting delay estimator and receiving an external signal returned by the deterministic network to obtain the network delay of the deterministic network; inputting the network delay and the power change amount of the power system into an augmented power system power response model to obtain a control offset; inputting the control offset into a state observer to obtain an estimated state variable; inputting the estimated state variable into a sequential state predictor to obtain a state variable under the network delay; inputting the state variable into a predictive model controller to obtain the power rate of the power system; integrating the power rate of the power system within a specified sampling period to obtain the required power amount of the power system within the specified sampling period to offset the frequency deviation of the power system. To overcome the problem of deterministic network delay attack in the power system and effectively offset the frequency deviation of the power system.

An electromagnetic field generation system and methods are provided. The system includes a plurality of parallel state predictors; and a plurality of switching components; outputs of the plurality of parallel state predictors being provided as inputs to the plurality of switching components. The method for generating an arbitrary electric current includes configuring a cost accumulator of a predictor unit to perform a cost function to obtain predicted accumulated difference of first derivative of an input variable against a reference; configuring the cost accumulator in another predictor unit to perform the cost function as a step function based on the input variable; and configuring the predictor unit to have a variable priority output as a linear function based on the input variable.

A tire replacement estimation system includes a tire supporting a vehicle. A sensor unit is mounted on the tire and includes a footprint centerline length measurement sensor and a pressure sensor. A processor is in electronic communication with the sensor unit and receives the measured centerline length and the measured pressure. An electronic vehicle network transmits selected vehicle parameters to the processor. A wear state predictor is stored on the processor and receives the measured centerline length, the measured pressure, and the selected vehicle parameters to generate an estimated wear state of the tire. An estimation model is stored on the processor and receives a plurality of estimated wear states of the tire and predicts a future wear state of the tire. The estimation model generates an estimated tire replacement date when the predicted future wear state of the tire exceeds a predetermined wear threshold.